The Transboundary Movement of Hazardous Solid Waste Between the United States and Mexico
Bibliographic record
Abstract
This study examines the transboundary movement of hazardous waste between the United States and Mexico and the consequences resulting from policy failures to regulate the trade. This issue continues to intensify despite measures and policies passed by the United States, impacting the public and environmental health of Mexican cities and communities near the United States - Mexico border. This research discusses the practice of hazardous waste trading, a global phenomenon, and focuses on the United States particularly because it is the largest exporter in the global waste trade. Focus is placed on U.S. trade with Mexico because although Canada imports the most waste from the U.S., Mexico receives the second most waste but cannot deal with it in an environmentally sound manner. Examination of the historical context influencing this trade relationship and policy framework is used to pinpoint weaknesses and potential leverage points to provide recommendations. Limited data on exports from the U.S. is publicly available on waste classification and volumes, restricting the research scope and being a policy failure itself. A content analysis of news sources and legal documentation provides information regarding the trade’s impact on communities. Residents near waste disposal sites report illnesses further exemplifying the need for stricter regulations within this trade to protect human health. Companies implicated include Tesla, Sally's Beauty Supply, and the U.S. Navy. This case study emphasizes the ethical and environmental risks of the global waste trade, placing responsibility on developed nations and corporations. As a part of this report, policy recommendations are made to further strengthen the framework of international policies, U.S. domestic policy, and the bilateral agreements with Mexico that regulate the waste trade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".